To leverage inertial, energetic, and cost benefits of small-scale robots,
critical future applications of autonomous technologies may depend on
coordinating large numbers of agents with minimal onboard sensing and
communication resources. However, a critical problem for autonomous multi-robot
groups is that state-of-the-art control schemes break down as robotic agents
are scaled down (decreasing agent resources) and the numerical size of
swarms is scaled up (increasing communication and coordination requirements)
[1]}, [2]}, [3]}, [4]}. NeuroSwarms addresses the
hypothesis that a similar distributed scaling problem may have been solved
by the evolved neural architecture of mammalian brains. Compared to signal
comprehension, signal production errors may be particularly deleterious to
large-scale, distributed and decentralized computations [5]}.
Thus, onboard suites for future ‘cognitive swarming’ platforms based on
NeuroSwarms principles should emphasize reliable transmission of low-bandwidth
data packets (e.g., spikes or continuous phase signals). Low fidelity inputs are
more easily compensated by distributed processing; i.e., sensor designs should
emphasize energy and cost to maximize deployment duration and swarm size.
